1. Field of the Invention
The present invention relates generally to a foreign language learning apparatus and method for correcting pronunciation through sentence input and, more particularly, to a foreign language learning apparatus and method for correcting pronunciation through sentence input, in which a waveform is disposed in accordance with a sentence input by a user and a matching percentage is calculated by matching a voice input by a user with the previously stored waveform or a waveform generated via a Text To Speech (TTS) engine.
2. Description of the Related Art
The present invention relates to a foreign language learning apparatus.
Recently, in line with the trend of the specialization and globalization of industry, the importance of learning a foreign language is increasing. In view of such importance, many people spend their time learning foreign languages, and thus various types of online and offline language learning courses have been established.
The importance of conversation in the field of the foreign language learning is further highlighted. In the case of English among foreign languages, TOEFL Test of Spoken English (TSE) was fully established in September of 2005. Accordingly, the demand for personal language learning materials and simulation test devices is increasing.
In general, pronunciation or the correction of pronunciation is performed using a 1:1 teaching method that involves a foreign lecturer. In such a case, problems arise in that high costs are required for English learning and the participation of people who lead a busy life, such as office workers, in the learning of English is very limited because education is performed according to specially arranged schedules.
Accordingly, there is a need for an education program which enables a user to effectively learn the pronunciation of a foreign language or pronunciation during his or her free time and to compare his or her pronunciation with a native speaker's pronunciation.
In order to meet the need, learning devices for language learning on which various programs for language learning using voice recognition have been installed have been developed and become popular.
A foreign language pronunciation evaluation method in such learning machines for language learning is based on a pronunciation comparison method using voice signal processing technology. In this method, programs for recognizing a learner's pronunciation using a Hidden Markov Model (hereinafter referred to as an “HMM”), comparing the learner's pronunciation with a native speaker's pronunciation, and providing notification of the results of the comparison are utilized.
In such a conventional foreign language pronunciation evaluation method, the accuracy of pronunciation is evaluated using a method of comparing one element characteristic of segmental characteristics for a learner's pronunciation with a corresponding characteristic of a native speaker. In particular, the phoneme characteristic data of the segmental characteristics is chiefly used.
The characteristic data of a native speaker uses characteristic data extracted via a trained Acoustic Model (AM) or from the native speaker's voice data when the native speaker pronounces expressions. However, disadvantages arise in that it is difficult to expect correct evaluation for the characteristic data extracted via the AM or from the voice because errors attributable to the personal pronunciation propensities of all native speakers are disregarded and the stress or isochronism of a syllable or sentence structure and a sentence cannot be evaluated or the accuracy of the evaluation cannot be expected even when the stress or isochronisms are evaluated.
Furthermore, the above-described pronunciation comparison and analysis programs are problematic in that they may not be used to evaluate individual characteristics, such the accent, stress, or pronunciation speed of a specific sentence or word, because the same or random weight is equally assigned to the segmental and non-segmental characteristics of all types of pronunciations.
Accordingly, most learning machines on which the programs have been installed receive a learner's voice corresponding to any one selected from among sentences displayed on a display device, and simply compare the received learner's voice with a native speaker's pronunciation and evaluate the results of the comparison using the programs, and provide the results of the evaluation to the learner in the form of scores.
Furthermore, a learner may be roughly aware of the degree of accuracy of his or her pronunciation indicated by the scores, but may not learn based on accurate comparison and analysis because there is no means for making a comparison on pronunciation for each field, such as each of the pronunciation length, accent, stress and pronunciation speed of a word or a sentence. As a result, a problem arises in that pronunciation correction is limited.
Furthermore, conventional language learning machines on which voice recognition programs or engines have been installed include only uniform evaluation criteria. Accordingly, conventional language learning machines are unable to enable learning suitable for the level or personality of a learner because the language ability or pronunciation characteristic of a learner are not appropriately reflected in the language learning machines. Furthermore, it is difficult to apply conventional voice recognizers to the AM of a foreign language native speaker, and a recognition ratio for the accuracy of pronunciation falls short of learners' expectations due to different pronunciation and language habits.
Furthermore, it is practically impossible to effectively distinguish minimal pairs, that is, an elementary learning target using conventional voice recognizers, it is impossible to effectively handle a noisy environment, and the detection performance of a keyword or core words and phrases is low or ineffective.
Furthermore, current language learning machines are problematic in that they cannot interpret and handle various language phenomena that may occur in actual conversation environments because the current language learning machines are focused on textbook learning methods.
Furthermore, conventional language learning machines are problematic in that they are expensive.
Accordingly, from a learner's viewpoint, the cost may be reduced if self-evaluation for pronunciation is performed using the Internet, but language learning systems using on-line methods, such as the Internet, are related to only English composition or conversations. Furthermore, there is no provision for a system for accurately evaluating a foreign language pronounced by a learner in various ways, providing notification of the differences between a learner's pronunciation and a native speaker's pronunciation, and digitizing the results of the evaluation.